Search Results for author:
Found 2 papers, 0 papers with code
Robust Noisy Label Learning via Two-Stream Sample Distillation
Noisy label learning aims to learn robust networks under the supervision of noisy labels, which plays a critical role in deep learning.
Pairwise Similarity Distribution Clustering for Noisy Label Learning
Noisy label learning aims to train deep neural networks using a large amount of samples with noisy labels, whose main challenge comes from how to deal with the inaccurate supervision caused by wrong labels.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
